Journal article
Spatial and temporal jitter distort estimated functional properties of visual sensory neurons
Journal of computational neuroscience, Vol.27(3), pp.309-319
12/2009
Handle:
https://hdl.handle.net/2376/111242
PMID: 19353259
Abstract
The functional properties of neural sensory cells or small neural ensembles are often characterized by analyzing response-conditioned stimulus ensembles. Many widely used analytical methods, like receptive fields (RF), Wiener kernels or spatio-temporal receptive fields (STRF), rely on simple statistics of those ensembles. They also tend to rely on simple noise models for the residuals of the conditional ensembles. However, in many cases the response-conditioned stimulus set has more complex structure. If not taken explicitly into account, it can bias the estimates of many simple statistics, and lead to erroneous conclusions about the functionality of a neural sensory system. In this article, we consider sensory noise in the visual system generated by small stimulus shifts in two dimensions (2 spatial or 1-space 1-time jitter). We model this noise as the action of a set of translations onto the stimulus that leave the response invariant. The analysis demonstrates that the spike-triggered average is a biased estimator of the model mean, and provides a de-biasing method. We apply this approach to observations from the stimulus/response characteristics of cells in the cat visual cortex and provide improved estimates of the structure of visual receptive fields. In several cases the new estimates differ substantially from the classic receptive fields, to a degree that may require re-evaluation of the functional description of the associated cells.
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Details
- Title
- Spatial and temporal jitter distort estimated functional properties of visual sensory neurons
- Creators
- Alexander G Dimitrov - Center for Computational Biology, Montana State University, Bozeman, MT 59717, USA. alex@cns.montana.eduMelissa A SheikoJonathan BakerShih-Cheng Yen
- Publication Details
- Journal of computational neuroscience, Vol.27(3), pp.309-319
- Academic Unit
- Mathematics and Statistics, Department of
- Publisher
- United States
- Identifiers
- 99900547214201842
- Language
- English
- Resource Type
- Journal article